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A Space-Bounded Anytime Algorithm for the Multiple Longest Common Subsequence Problem.

Jiaoyun Yang1, Yun Xu2, Yi Shang3

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Summary
This summary is machine-generated.

New algorithms for the multiple longest common subsequence (MLCS) problem offer significant space and time efficiency. These anytime algorithms, SA-MLCS and SLA-MLCS, outperform existing methods for large-scale sequence similarity identification.

Keywords:
Heuristic searchanytime algorithmmultiple longest common subsequence (MLCS)space bounded

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Computer Science

Background:

  • The multiple longest common subsequence (MLCS) problem is crucial for sequence similarity identification across various scientific domains.
  • Existing exact MLCS algorithms struggle with large datasets due to their NP-hard nature, necessitating efficient solutions.
  • While anytime algorithms address time constraints, space-efficient MLCS algorithms remain underdeveloped.

Purpose of the Study:

  • To develop novel, space-efficient anytime algorithms for the multiple longest common subsequence (MLCS) problem.
  • To address the limitations of existing algorithms in handling large-scale sequence data within memory constraints.

Main Methods:

  • Formulated the MLCS problem as a graph search problem.
  • Introduced SA-MLCS, an anytime algorithm employing iterative beam widening for reduced space usage.
  • Developed SLA-MLCS, a space-bounded extension of SA-MLCS incorporating a replacing strategy to manage memory limits.

Main Results:

  • SA-MLCS and SLA-MLCS demonstrated an order of magnitude reduction in space and time compared to MLCS-APP, while yielding superior solutions.
  • SA-MLCS and SLA-MLCS successfully processed instances an order of magnitude larger than those handled by Pro-MLCS.
  • SLA-MLCS achieved significantly better solutions than SA-MLCS on large-scale instances.

Conclusions:

  • SA-MLCS and SLA-MLCS represent significant advancements in space-efficient MLCS algorithms.
  • These algorithms offer practical solutions for real-world applications requiring efficient sequence similarity analysis.
  • SLA-MLCS provides a robust approach for tackling large instances where memory is a critical factor.